With the rapid advances in sensor design, data storage capabilities, and network connectivity for industrial assets, there is an explosive growth in the amount of complex, multivariate time series sensor data available. This data can be used to analyze the failure of an asset.
Failure analytics can be understood as a knowledge discovery process where the output gives the fault signatures related to the failure, while the input corresponds to observed/monitored sensor recording data. For fleet-level analytics, high-frequency sampling rate, large-scale input data is often available. This large quantity of data can include observations from failed industrial assets, but also observations from healthy industrial assets. Typically, the dataset can contain much more observations from healthy industrial equipment.
FIGS. 1A-1B depict representative illustrations of monitored sensor data 100, 110, 150 respectively for features A, B, and C of an industrial asset. Fault signatures 120, 170 each have different lead time to failure depending on the monitored feature of the industrial asset. Signature 110 (FIG. 1A) appears fifteen days before final failure and has a duration of two days. FIG. 1A illustrates that the fault causes a small perturbation on the value of feature A; and a large perturbation on the value of feature B. FIG. 1B illustrates signature 160 appearing almost one month before final failure of feature C and lasting for five days. There is a level-shift on feature C, which can be captured by a sliding-window based autocorrelation technique (“auc”).
Observable in FIGS. 1A-1B is that a lag (sometimes of significant duration) can exist between occurrence of the fault signature and the final system failure. Also, the time of the signature duration can vary across the type and nature of the industrial asset and the particular feature. In real world industrial problems, such “short” signature and “lag” patterns are usual and could be of different reasons.
A feature monitored by a sensor could be indirectly related to the failure of the industrial asset. For example, an engine failure might happen because an engine component cracks (i.e., the failure's root cause). But due to the complexity of the engine design, an interval of time can elapse prior to the impact of the fault propagating through the whole system and, thus, leading to final failure. Prior to the component cracking, a vibration sensor monitoring that particular component can capture a pattern of strong vibrations. This pattern of vibrations can be viewed as the fault signature. Once the crack happens, this component can become unconnected from the whole system, at which time the component's vibration profile can become (and remain) stable until the final failure. Therefore, the timestamps of the signature might not be adjacent to the final failure—but only present at the time the component cracks.
Because of industrial asset system complexities, this component crack might be one among many possible root causes of a final failure. Under conventional approaches it is extremely difficult for domain experts or existing machine learning techniques to allocate such relevant signatures as the failure's root cause in highly complicated and noisy, multi-asset environments that produce a large quantity of multivariate temporal data streams from a number of sensors monitoring the industrial asset.
Conventional analytics can identify fleet-level fault signatures when related to an identified failure. Such signature should consist of or directly relate to the input sensor weighting, in order to support root cause detection. Besides fleets of industrial assets generate a large volume of data, there are many challenges in identifying failure event signatures. For example, the monitored sensor dataset is multivariate and includes data from multiple industrial assets. Conventional approaches implementing univariate and single-asset analytics single-asset analytics are insufficient to provide accurate analysis. Also, even though the observed event time is known, the time that the fault signatures appears is usually unknown, and could be different across different events/assets. Conventional (semi-) supervised machine learning techniques can provide dubious analytical results from this condition.